Real-time object tracking from corners

Robotica ◽  
1998 ◽  
Vol 16 (1) ◽  
pp. 109-116 ◽  
Author(s):  
Han Wang ◽  
Choon Seng Chua ◽  
Ching Tong Sim
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Affine Transformation ◽  
Feature Detection ◽  
Moving Objects ◽  
Tracking System ◽  
Computational Time ◽  
Unexpected Events ◽  
Robot Head ◽  
Two Stages

This paper reports a visual tracking system that can track moving objects in real-time with a modest workstation equipped with a pan-tilt device. The algorithm essentially has three parts: (1) feature detection, (2) tracking and (3) control of the robot head. Corners are viewpoint invariant, hence being utilised as the beacon for tracking. Tracking is performed in two stages of Kalman filtering and affine transformation. A technique of reducing greatly the computational time for the correlaton is also described. The Kalman filter predicts intelligently the fovea window and reduced computation dramatically. The affine transformation deals with the unexpected events when there is partial occlusion.

scholarly journals Segmentation of Moving Objects using Numerous Background Subtraction Methods for Surveillance Applications

2020 ◽  
Vol 9 (3) ◽  
pp. 2553-2563
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Tracking System ◽  
Real Life ◽  
Ground Truth ◽  
Computational Time ◽  
Dynamic Background ◽  
Illumination Changes ◽  
Benchmark Datasets ◽  
Surveillance Applications

Background subtraction is a key part to detect moving objects from the video in computer vision field. It is used to subtract reference frame to every new frame of video scenes. There are wide varieties of background subtraction techniques available in literature to solve real life applications like crowd analysis, human activity tracking system, traffic analysis and many more. Moreover, there were not enough benchmark datasets available which can solve all the challenges of subtraction techniques for object detection. Thus challenges were found in terms of dynamic background, illumination changes, shadow appearance, occlusion and object speed. In this perspective, we have tried to provide exhaustive literature survey on background subtraction techniques for video surveillance applications to solve these challenges in real situations. Additionally, we have surveyed eight benchmark video datasets here namely Wallflower, BMC, PET, IBM, CAVIAR, CD.Net, SABS and RGB-D along with their available ground truth. This study evaluates the performance of five background subtraction methods using performance parameters such as specificity, sensitivity, FNR, PWC and F-Score in order to identify an accurate and efficient method for detecting moving objects in less computational time.

scholarly journals Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
A. Sarno ◽  
E. Andreozzi ◽  
D. De Caro ◽  
G. Di Meo ◽  
A. G. M. Strollo ◽  
...  
Keyword(s):  
Image Quality ◽  
Real Time ◽  
Video Processing ◽  
Moving Objects ◽  
Quantum Noise ◽  
Moving Average ◽  
Threshold Level ◽  
Computational Time ◽  
X Ray ◽  
Mask Size

Abstract Background Quantum noise intrinsically limits the quality of fluoroscopic images. The lower is the X-ray dose the higher is the noise. Fluoroscopy video processing can enhance image quality and allows further patient’s dose lowering. This study aims to assess the performances achieved by a Noise Variance Conditioned Average (NVCA) spatio-temporal filter for real-time denoising of fluoroscopic sequences. The filter is specifically designed for quantum noise suppression and edge preservation. It is an average filter that excludes neighborhood pixel values exceeding noise statistic limits, by means of a threshold which depends on the local noise standard deviation, to preserve the image spatial resolution. The performances were evaluated in terms of contrast-to-noise-ratio (CNR) increment, image blurring (full width of the half maximum of the line spread function) and computational time. The NVCA filter performances were compared to those achieved by simple moving average filters and the state-of-the-art video denoising block matching-4D (VBM4D) algorithm. The influence of the NVCA filter size and threshold on the final image quality was evaluated too. Results For NVCA filter mask size of 5 × 5 × 5 pixels (the third dimension represents the temporal extent of the filter) and a threshold level equal to 2 times the local noise standard deviation, the NVCA filter achieved a 10% increase of the CNR with respect to the unfiltered sequence, while the VBM4D achieved a 14% increase. In the case of NVCA, the edge blurring did not depend on the speed of the moving objects; on the other hand, the spatial resolution worsened of about 2.2 times by doubling the objects speed with VBM4D. The NVCA mask size and the local noise-threshold level are critical for final image quality. The computational time of the NVCA filter was found to be just few percentages of that required for the VBM4D filter. Conclusions The NVCA filter obtained a better image quality compared to simple moving average filters, and a lower but comparable quality when compared with the VBM4D filter. The NVCA filter showed to preserve edge sharpness, in particular in the case of moving objects (performing even better than VBM4D). The simplicity of the NVCA filter and its low computational burden make this filter suitable for real-time video processing and its hardware implementation is ready to be included in future fluoroscopy devices, offering further lowering of patient’s X-ray dose.

Design of a Real-Time Tracking System for Fast-moving Objects

1997 ◽  
Vol 43 (5) ◽  
pp. 359-369 ◽  
Author(s):  
A K Rastogi ◽  
B N Chatterji ◽  
A K Ray
Keyword(s):  
Real Time ◽  
Fast Moving ◽  
Moving Objects ◽  
Tracking System ◽  
Real Time Tracking

Real-Time Monocular Three-Dimensional Motion Tracking Using a Multithread Active Vision System

2018 ◽  
Vol 30 (3) ◽  
pp. 453-466 ◽  
Author(s):  
Shaopeng Hu ◽  
◽  
Mingjun Jiang ◽  
Takeshi Takaki ◽  
Idaku Ishii
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Motion Tracking ◽  
Moving Objects ◽  
Vision System ◽  
Tracking System ◽  
Three Dimensional ◽  
Active Vision ◽  
Left And Right ◽  
Motion Tracking System

In this study, we developed a monocular stereo tracking system to be used as a marker-based, three-dimensional (3-D) motion capture system. This system aims to localize dozens of markers on multiple moving objects in real time by switching five hundred different views in 1 s. The ultrafast mirror-drive active vision used in our catadioptric stereo tracking system can accelerate a series of operations for multithread gaze control with video shooting, computation, and actuation within 2 ms. By switching between five hundred different views in 1 s, with real-time video processing for marker extraction, our system can function asJvirtual left and right pan-tilt tracking cameras, operating at 250/Jfps to simultaneously capture and processJpairs of 512 × 512 stereo images with different views via the catadioptric mirror system. We conducted several real-time 3-D motion experiments to capture multiple fast-moving objects with markers. The results demonstrated the effectiveness of our monocular 3-D motion tracking system.

A Real-Time System for Pedestrian Detection Based on GMM and HOG

2011 ◽  
Vol 403-408 ◽  
pp. 2723-2727
Author(s):  
Feng Li ◽  
Yi Feng Zou ◽  
He Qin Zhou ◽  
Guan Jun Pei
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Tracking System ◽  
Pedestrian Detection ◽  
Gaussian Mixture ◽  
Video Sequences ◽  
Surveillance Systems ◽  
Real Time System ◽  
Efficient System ◽  
Histograms Of Oriented Gradients

The detection of pedestrian which has been widely used in digital surveillance systems is a popular topic in computer vision. This paper mainly discusses a system of pedestrian detection in video sequences captured from a stationary camera hanging in a public scene. We describe an efficient system combining background subtraction based on Gaussian Mixture Model (GMM) and object classification based on Histograms of Oriented Gradients (HOG). We first process moving objects segmentation using GMM. Then a HOG detector is used to classify the moving objects into person and none-person. Experimental results on video sequences have demonstrated that the real-time tracking system can process 15 to 30 frames per second robustly with a high accuracy.

GPS and GSM Based Tracking System for Objects Moving over Wide Geographical Areas

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fatima Ameen ◽  
Ziad Mohammed ◽  
Abdulrahman Siddiq
Keyword(s):  
Global Positioning System ◽  
Mobile Communications ◽  
Moving Objects ◽  
Tracking System ◽  
Geographical Area ◽  
Tracking Systems ◽  
Acceptable Result ◽  
Internet Service ◽  
Global Positioning ◽  
Tracking Objects

Tracking systems of moving objects provide a useful means to better control, manage and secure them. Tracking systems are used in different scales of applications such as indoors, outdoors and even used to track vehicles, ships and air planes moving over the globe. This paper presents the design and implementation of a system for tracking objects moving over a wide geographical area. The system depends on the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies without requiring the Internet service. The implemented system uses the freely available GPS service to determine the position of the moving objects. The tests of the implemented system in different regions and conditions show that the maximum uncertainty in the obtained positions is a circle with radius of about 16 m, which is an acceptable result for tracking the movement of objects in wide and open environments.

Real-Time Moving Objects Tracking for Distributed Smart Video Surveillances

2016 ◽  
Vol 11 (4) ◽  
pp. 324
Author(s):  
Nor Nadirah Abdul Aziz ◽  
Yasir Mohd Mustafah ◽  
Amelia Wong Azman ◽  
Amir Akramin Shafie ◽  
Muhammad Izad Yusoff ◽  
...  
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Objects Tracking

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

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